{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一元线性回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基本形式\n",
    "$$\\tag{1}\n",
    "f(\\mathbf{x}) = w_1 x_1 + w_2 x_2 + ... + w_d x_d + b\n",
    "$$\n",
    "向量形式\n",
    "$$\\tag{2}\n",
    "f(\\mathbf{x}) = \\mathbf{w}^\\mathrm{T}\\mathbf{x}+b\n",
    "$$\n",
    "其中$\\mathbf{w} = (w_1; w_2; ...; w_d)$.\n",
    "\n",
    "针对一元线性回归，我们有\n",
    "$$\\tag{3}\n",
    "f(x) = w x + b$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 读取数据\n",
    "数据处理包括：数据分析、清理，数据集划分等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-27T06:31:44.982355Z",
     "start_time": "2019-09-27T06:31:44.979358Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-27T06:31:46.491357Z",
     "start_time": "2019-09-27T06:31:45.135356Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 使用pandas读取csv数据\n",
    "data = pd.read_csv('data/kaggle_house_price_prediction/kaggle_hourse_price_train.csv')\n",
    "# 打印前5行\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-27T06:31:46.542355Z",
     "start_time": "2019-09-27T06:31:46.501361Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 丢弃有缺失值的特征（列）\n",
    "data.dropna(axis = 1, inplace = True)\n",
    "\n",
    "# 只保留整数的特征\n",
    "data = data[[col for col in data.dtypes.index if data.dtypes[col] == 'int64']]\n",
    "# 上下等价\n",
    "idx_list = []\n",
    "for col in data.dtypes.index:\n",
    "    if data.dtypes[col] == 'int64':\n",
    "        idx_list.append(col)\n",
    "data = data[idx_list]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本题的要求是，针对LotArea, BsmtUnfSF, GarageArea三个特征，使用训练集训练三个一元线性回归模型，对比其在测试集上的MAE和RMSE，并且要绘制曲线。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "所以我们只保留这三列与标记这一列，总共四列的数据即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-27T06:31:46.566358Z",
     "start_time": "2019-09-27T06:31:46.547358Z"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LotArea</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8450</td>\n",
       "      <td>150</td>\n",
       "      <td>548</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9600</td>\n",
       "      <td>284</td>\n",
       "      <td>460</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11250</td>\n",
       "      <td>434</td>\n",
       "      <td>608</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9550</td>\n",
       "      <td>540</td>\n",
       "      <td>642</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14260</td>\n",
       "      <td>490</td>\n",
       "      <td>836</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LotArea  BsmtUnfSF  GarageArea  SalePrice\n",
       "0     8450        150         548     208500\n",
       "1     9600        284         460     181500\n",
       "2    11250        434         608     223500\n",
       "3     9550        540         642     140000\n",
       "4    14260        490         836     250000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 地块尺寸 未完成的地下室平方英尺 车库的面积\n",
    "features = ['LotArea', 'BsmtUnfSF', 'GarageArea']\n",
    "target = 'SalePrice'\n",
    "data = data[features + [target]]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 打乱数据顺序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-27T06:31:48.231443Z",
     "start_time": "2019-09-27T06:31:48.226437Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-27T06:31:48.586440Z",
     "start_time": "2019-09-27T06:31:48.580447Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_shuffled = shuffle(data, random_state = 32) # 这个32不要改变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-27T06:31:57.349293Z",
     "start_time": "2019-09-27T06:31:57.338331Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LotArea</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>1680</td>\n",
       "      <td>321</td>\n",
       "      <td>264</td>\n",
       "      <td>118000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>32668</td>\n",
       "      <td>816</td>\n",
       "      <td>484</td>\n",
       "      <td>200624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1394</th>\n",
       "      <td>4045</td>\n",
       "      <td>286</td>\n",
       "      <td>648</td>\n",
       "      <td>246578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>13891</td>\n",
       "      <td>1734</td>\n",
       "      <td>1020</td>\n",
       "      <td>582933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>9548</td>\n",
       "      <td>458</td>\n",
       "      <td>613</td>\n",
       "      <td>237000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      LotArea  BsmtUnfSF  GarageArea  SalePrice\n",
       "363      1680        321         264     118000\n",
       "529     32668        816         484     200624\n",
       "1394     4045        286         648     246578\n",
       "803     13891       1734        1020     582933\n",
       "832      9548        458         613     237000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_shuffled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 取前70%的数据为训练集，后30%为测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-27T06:42:25.586080Z",
     "start_time": "2019-09-27T06:42:25.577116Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_of_samples = data_shuffled.shape[0]\n",
    "split_line = int(num_of_samples * 0.7)\n",
    "train_data = data.iloc[:split_line]\n",
    "test_data = data.iloc[split_line:]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-27T06:31:58.497295Z",
     "start_time": "2019-09-27T06:31:58.490292Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1021, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-27T06:31:58.945295Z",
     "start_time": "2019-09-27T06:31:58.938293Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(439, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 编写模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们以类(class)的形式编写这个模型，python中的类很简单，只不过这个类里面需要调用两个函数，一个是get_w，这个是计算模型w的函数，另一个是get_b，计算模型b的函数，需要大家来完成。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本实验要求使用最小二乘法求解一元线性回归模型  \n",
    "求解$w$和$b$使均方误差$E_{(w,b)} = \\sum^m_{i=1}(y_i - wx_i - b)^2$最小化的过程，称为线性回归模型的最小二乘“参数估计”(parameter estimation)。我们可将$E_{(w,b)}$分别对$w$和$b$求导，得到\n",
    "$$\\tag{4}\n",
    "\\frac{\\partial E_{(w,b)}}{\\partial w} = 2(w \\sum^m_{i=1} x^2_i - \\sum^m_{i=1} (y_i - b) x_i),\n",
    "$$\n",
    "\n",
    "$$\\tag{5}\n",
    "\\frac{\\partial E_{(w,b)}}{\\partial b} = 2(mb - \\sum^m_{i=1}(y_i - w x_i))\n",
    "$$\n",
    "\n",
    "然后令式(4)和式(5)为0，可得到$w$和$b$的闭式解(closed-form solution)\n",
    "$$\\tag{6}\n",
    "w = \\frac{\\sum^m_{i=1} y_i(x_i - \\bar{x})}{\\sum^m_{i=1}x^2_i - \\frac{1}{m}(\\sum^m_{i=1}x_i)^2}\n",
    "$$\n",
    "\n",
    "$$\\tag{7}\n",
    "b = \\frac{1}{m}\\sum^m_{i=1}(y_i - w x_i)\n",
    "$$\n",
    "其中，$\\bar{x} = \\frac{1}{m}\\sum^m_{i=1}x_i$为$x$的均值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先编写求解w的函数，传入的参数就是x和y，都是np.ndarray类型的，或是pd.Series类型的（其实都一样）。我们需要大家在下面完成式(6)和式(7)的求解过程，将计算得到的w和b的值返回"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_w(x, y):\n",
    "    '''\n",
    "    这个函数是计算模型w的值的函数，\n",
    "    传入的参数分别是x和y，表示数据与标记\n",
    "    \n",
    "    Parameter\n",
    "    ----------\n",
    "        x: np.ndarray，pd.Series，传入的特征数据\n",
    "\n",
    "        y: np.ndarray, pd.Series，对应的标记\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "        w: float, 模型w的值\n",
    "    '''\n",
    "    \n",
    "    # m表示样本的数量\n",
    "    m = x.shape[0]\n",
    "    \n",
    "    # 求x的均值\n",
    "    x_mean = sum(x) / m;\n",
    "    \n",
    "    # 求w的分子部分\n",
    "    numerator = sum((x - x_mean) * y )\n",
    "    \n",
    "    # 求w的分母部分\n",
    "    denominator = sum(x * x) - sum(x) * sum(x) / m\n",
    "    \n",
    "    # 求w\n",
    "    w = numerator / denominator\n",
    "    \n",
    "    # 返回w\n",
    "    return w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_b(x, y, w):\n",
    "    '''\n",
    "    这个函数是计算模型b的值的函数，\n",
    "    传入的参数分别是x, y, w，表示数据，标记以及模型的w值\n",
    "    \n",
    "    Parameter\n",
    "    ----------\n",
    "        x: np.ndarray，pd.Series，传入的特征数据\n",
    "\n",
    "        y: np.ndarray, pd.Series，对应的标记\n",
    "        \n",
    "        w: np.ndarray, pd.Series，模型w的值\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "        b: float, 模型b的值\n",
    "    '''\n",
    "    # 样本个数\n",
    "    m = x.shape[0]\n",
    "    \n",
    "    # 求b\n",
    "    b = sum(y - w * x) / m\n",
    "    \n",
    "    # 返回b\n",
    "    return b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面这个类，就是一个最简单的一元线性回归的类，我们已经帮你实现好了三个方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class myLinearRegression:\n",
    "    def __init__(self):\n",
    "        '''\n",
    "        类的初始化方法，不需要初始化的参数\n",
    "        这里设置了两个成员变量，用来存储模型w和b的值\n",
    "        '''\n",
    "        self.w = None\n",
    "        self.b = None\n",
    "    \n",
    "    def fit(self, x, y):\n",
    "        '''\n",
    "        这里需要编写训练的函数，也就是调用模型的fit方法，传入特征x的数据和标记y的数据\n",
    "        这个方法就可以求解出w和b\n",
    "        '''\n",
    "        self.w = get_w(x, y)\n",
    "        self.b = get_b(x, y, self.w)\n",
    "        \n",
    "    def predict(self, x):\n",
    "        '''\n",
    "        这是预测的函数，传入特征的数据，返回模型预测的结果\n",
    "        '''\n",
    "        if self.w == None or self.b == None:\n",
    "            print(\"模型还未训练，请先调用fit方法训练\")\n",
    "            return \n",
    "        \n",
    "        return self.w * x + self.b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 创建一个模型的实例\n",
    "model1 = myLinearRegression()\n",
    "\n",
    "# 使用训练集对模型进行训练，传入训练集的LotArea和标记SalePrice\n",
    "model1.fit(train_data['LotArea'], train_data['SalePrice'])\n",
    "\n",
    "# 对测试集进行预测，并将结果存储在变量prediction中\n",
    "prediction1 = model1.predict(test_data['LotArea'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 性能度量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型训练完成后，还需要在测试集上验证其预测能力，这就需要计算模型的一些性能指标，如MAE和RMSE等。\n",
    "\n",
    "$$\\tag{8}\n",
    "MAE(\\hat{y}, y) = \\frac{1}{m} \\sum^m_{i=1} \\vert \\hat{y} - y \\vert\n",
    "$$\n",
    "\n",
    "$$\\tag{9}\n",
    "RMSE(\\hat{y}, y) = \\sqrt{\\frac{1}{m} \\sum^m_{i=1} (\\hat{y} - y)^2}\n",
    "$$\n",
    "其中，$\\hat{y}$是模型的预测值，$y$是真值，$m$是样本数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def MAE(y_hat, y):\n",
    "    # 请你完成MAE的计算过程\n",
    "    # YOUR CODE HERE\n",
    "    m=y.shape[0]\n",
    "    mae = sum(abs(y_hat - y)) / m\n",
    "    return mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def RMSE(y_hat, y):\n",
    "    # 请你完成RMSE的计算过程\n",
    "    # YOUR CODE HERE\n",
    "    m=y.shape[0]\n",
    "    rmase = (sum((y_hat - y) * (y_hat - y)) / m ) ** 0.5\n",
    "    return rmase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在此计算出模型在测试集上的MAE与RMSE值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型1，特征：LotArea\n",
      "('MAE:', 54676)\n",
      "('RMSE:', 76827.903283898093)\n"
     ]
    }
   ],
   "source": [
    "mae1 = MAE(prediction1, test_data['SalePrice'])\n",
    "rmse1 = RMSE(prediction1, test_data['SalePrice'])\n",
    "print(\"模型1，特征：LotArea\")\n",
    "print(\"MAE:\", mae1)\n",
    "print(\"RMSE:\", rmse1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 模型预测效果可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xc049048>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iIiIiIiISUJIsIiIiIiIiElCSLCIiIiIiIhJQkiwiIiIiIiISUJIsIiIiIiIi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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbf81278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建新的图\n",
    "plt.figure(figsize = (16, 6))\n",
    "\n",
    "# 创建子图1\n",
    "plt.subplot(121) # 121分别代表，生成1行2列个图，这是第1个\n",
    "# 其中的参数为横轴值，纵轴值，label为此条曲线的标签， '.' 表示画出的图的图形为散点图\n",
    "plt.plot(train_data['LotArea'].values, train_data['SalePrice'].values, '.', label = 'training data')\n",
    "# '-' 表示画出的图形为折线图\n",
    "plt.plot(train_data['LotArea'].values, model1.predict(train_data['LotArea']), '-', label = 'prediction')\n",
    "plt.xlabel(\"LotArea\")\n",
    "plt.ylabel('SalePrice')\n",
    "plt.title(\"training set\")\n",
    "plt.legend()\n",
    "\n",
    "# 创建子图2\n",
    "plt.subplot(122) # 121分别代表，生成1行2列个图，这是第2个\n",
    "plt.plot(test_data['LotArea'].values, test_data['SalePrice'].values, '.', label = 'testing data')\n",
    "plt.plot(test_data['LotArea'].values, prediction1, '-', label = 'prediction')\n",
    "plt.xlabel(\"LotArea\")\n",
    "plt.ylabel('SalePrice')\n",
    "plt.title(\"testing set\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 通过左右两图的对比，分析该模型出现的问题，并给出能帮助模型更好的做预测的方案(选做)\n",
    "###### 双击此处展开讨论\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用BsmtUnfSF作为特征，完成模型的训练，指标计算，可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型2，特征：BsmtUnfSF\n",
      "('MAE:', 53942)\n",
      "('RMSE:', 76899.285718399231)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xc550198>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iIiIiUjglyQ1AjykSEREREREpjLpbi4iIiIiIiASUJIuIiIiIiIgElCSLiIiI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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc336a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "\n",
    "# 创建一个模型的实例\n",
    "model1 = myLinearRegression()\n",
    "\n",
    "# 使用训练集对模型进行训练，传入训练集的LotArea和标记SalePrice\n",
    "model1.fit(train_data['BsmtUnfSF'], train_data['SalePrice'])\n",
    "\n",
    "# 对测试集进行预测，并将结果存储在变量prediction中\n",
    "prediction1 = model1.predict(test_data['BsmtUnfSF'])\n",
    "\n",
    "def MAE(y_hat, y):\n",
    "    # 请你完成MAE的计算过程\n",
    "    # YOUR CODE HERE\n",
    "    m=y.shape[0]\n",
    "    mae = sum(abs(y_hat - y)) / m\n",
    "    return mae\n",
    "\n",
    "def RMSE(y_hat, y):\n",
    "    # 请你完成RMSE的计算过程\n",
    "    # YOUR CODE HERE\n",
    "    m=y.shape[0]\n",
    "    rmase = (sum((y_hat - y) * (y_hat - y)) / m ) ** 0.5\n",
    "    return rmase\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "mae1 = MAE(prediction1, test_data['SalePrice'])\n",
    "rmse1 = RMSE(prediction1, test_data['SalePrice'])\n",
    "print(\"模型2，特征：BsmtUnfSF\")\n",
    "print(\"MAE:\", mae1)\n",
    "print(\"RMSE:\", rmse1)\n",
    "\n",
    "# 创建新的图\n",
    "plt.figure(figsize = (16, 6))\n",
    "\n",
    "# 创建子图1\n",
    "plt.subplot(121) # 121分别代表，生成1行2列个图，这是第1个\n",
    "# 其中的参数为横轴值，纵轴值，label为此条曲线的标签， '.' 表示画出的图的图形为散点图\n",
    "plt.plot(train_data['BsmtUnfSF'].values, train_data['SalePrice'].values, '.', label = 'training data')\n",
    "# '-' 表示画出的图形为折线图\n",
    "plt.plot(train_data['BsmtUnfSF'].values, model1.predict(train_data['BsmtUnfSF']), '-', label = 'prediction')\n",
    "plt.xlabel(\"BsmtUnfSF\")\n",
    "plt.ylabel('SalePrice')\n",
    "plt.title(\"training set\")\n",
    "plt.legend()\n",
    "\n",
    "# 创建子图2\n",
    "plt.subplot(122) # 121分别代表，生成1行2列个图，这是第2个\n",
    "plt.plot(test_data['BsmtUnfSF'].values, test_data['SalePrice'].values, '.', label = 'testing data')\n",
    "plt.plot(test_data['BsmtUnfSF'].values, prediction1, '-', label = 'prediction')\n",
    "plt.xlabel(\"BsmtUnfSF\")\n",
    "plt.ylabel('SalePrice')\n",
    "plt.title(\"testing set\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用GarageArea作为特征，完成模型的训练，指标计算，可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型1，特征：GarageArea\n",
      "('MAE:', 43110)\n",
      "('RMSE:', 65481.489667449823)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xc7755c0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SUJIsIiIiIiIiElCSLCIiIiIiIhJQkiwiIiIiIiISUJIsIiIiIiIiEshokmxm\ny8zsLTN7w8yqg7IdzOw5M1sU/C6N2P9KM1tsZu+b2bER5ZXBeRab2c1mZkH5Vmb2aFA+z8wGRRwz\nLviMRWY2LpPXKSIiUigUm0VERNqXjZ7kke4+zN1HBO+vAP7u7nsBfw/eY2b7AGcA5cBo4FYzKw6O\n+RMwAdgr+BkdlJ8H1Lv7nsCNwG+Dc+0ATAYOBg4CJkcGfBERSUzN8nqmzllMzfL6XFdF0kuxWURE\nJI5cDLc+Ebg/eH0/cFJE+SPuvtndlwKLgYPMrD+wvbvPdXcH/hx1TPhcM4CjgpbsY4Hn3H2Du9cD\nz/FV8BYRkQTULK/nrLvm8vtn3+esu+YqUe7aFJtFREQCmU6SHXjezGrM7PygbGd3XxO8/hDYOXi9\nK7Ay4tjaoGzX4HV0eatj3L0R+Bgoa+dcrZjZ+WZWbWbV69at69wVioh0UXOX1NHQ2Eyzw5bGZuYu\nqct1lSQ9FJtFRETaUZLh83/L3VeZ2deA58zsvciN7u5m5hmuQ1zufgdwB8CIESNyVg8RkXxUNbiM\nniVFbGlspkdJEVWDy3JdJUkPxWYREZF2ZLQn2d1XBb/XAk8QmoP0UTBMi+D32mD3VcBuEYcPCMpW\nBa+jy1sdY2YlQB+grp1ziYhIgioHljJtfBWXjBrCtPFVVA7U9NGuQLFZRESkfRlLks1sWzPrHX4N\njAIWAk8C4RUtxwGzgtdPAmcEq2LuQWgRkH8Fw78+MbOqYE7TOVHHhM91KvBCMDfqGWCUmZUGi4KM\nCspERCQJlQNLmThyTyXIXYRis4iISMcyOdx6Z+CJ4IkQJcBD7v43M5sPPGZm5wHLgdMB3P1tM3sM\neAdoBCa6e1NwrouA+4BtgNnBD8DdwANmthjYQGgFTtx9g5n9Cpgf7DfF3Tdk8FpFREQKgWKziIhI\nByzUuCsjRozw6urqXFdDRBJQs7yeuUvqqBpcph5OyVtmVhPxiCXpBMVmERFJp0Rjc6YX7hIRSavw\nY4kaGpvpWVKkubIiIiIikla5eE6yiEin6bFEIiIiIpJJSpJFpKCEH0tUbOixRCIiIiKSdhpuLSIF\nJfxYoug5yZqnLCIiIiLpoCRZRApO5cDSVomw5imLiIiISLpouLWIFDzNUxYRERGRdFGSLCIFT/OU\nRURERCRdNNxaRApevHnKIiIikrpk1/3QOiFS6JQki0iXED1PWURERFKX7LofWidEugINtxYRERER\nkZiSXfdCC6EBAAAgAElEQVRD64RIV6AkWUREREREYkp23Q+tEyJdgYZbi4iIiIhITMmu+6F1QqQr\nUJIsIl2CFgkRERHJjGTX/dA6IVLolCSLSMHTIiEiIiIiki6akywiBU+LhIiIiIhIuihJFpGCF2+R\nkJrl9Uyds5ia5fU5rqGIiIiIFAoNtxaRghdrkRANwRYRERGRzkg4STazbwF7ufu9ZrYTsJ27L81c\n1UREEhe9SEisIdiJJMlaAEwKiWKziIhI+iWUJJvZZGAEMAS4F+gBPAgcmrmqiUimpCMRzPdkMjwE\ne0tjc8LPaVTvsxQSxWYREZHMSLQn+XvAAcACAHdfbWa9M1YrEcmYdCSChZBMduY5jZ3tfRbJEcVm\nERGRDEh04a4Gd3fAAcxs28xVSUQyKR0rQRfKatKVA0uZOHLPhBPdeAuAieQpxWYREZEMSLQn+TEz\nux3oa2YTgB8Bd2auWiLdV6aHMXdmGHImzpGPOtP7LJJDis0iIiIZYKFG6AR2NDsGGAUY8Iy7P5fJ\nimXbiBEjvLq6OtfVkG4uW8OYu8OcZJFcM7Madx+R4c9QbBYREUlQorE50YW79gBeDgdfM9vGzAa5\n+7LUqikikbI1JzZ6JehcnSPdlLhLd6LYLCIikhmJzkmeDjRHvG8KykQkjTQntvPCvfC/f/Z9zrpr\nLjXL63NdpYJQs7yeqXMW6/sqTIrNIiIiGZDonOQSd28Iv3H3BjPrmaE6iXRbmhPbeVqZOnmFsEq5\ntEuxWUQkSzRarXtJNEleZ2bfdfcnAczsRGB95qol0n3l4zDmQtBVFxPLJDUsFDzFZhGRLFCjcveT\naJL8Y2Camf2R0OIgK4FzMlYrESlouWhtVS988tSwUPAUm0VEskCNyt1PQkmyu38AVJnZdsH7zzJa\nKxEpWA/NW8GkWQtpds96a6t64ZOjhoXCptgsIpIdalTOoc2fwoIHYN/TYLudsvax7SbJZna2uz9o\nZpdElQPg7jdksG4iUmBqltczadZCGptDj5ZrUGtr3lPDQuFRbBYRyS41KufAslfgvuO/et+zF1Se\nm7WP76gnedvgd+9MV0RECt/cJXU0NX/17PUiM7W2SmFo2AR/vQTefBjOeRIGH57rGrVHsVlEJMvU\nqJwFTVvgb1fC/Dtblx9yMRzwg6xWpd0k2d1vN7Ni4BN3vzFLdRKRAlU1uIytehTRsKWZoiJjyokV\nCiiS3957Gh45M9e1SIpis0jXp5WUpVtZ+y7ccyx8+XHr8h89C7sfnJMqdTgn2d2bzOxMoFOBOAjk\n1cAqdx9jZjsAjwKDgGXA6e5eH+x7JXAeoWc9/h93fyYorwTuA7YBngZ+6u5uZlsBfwYqgTpgrLsv\nC44ZB1wVVOPX7n5/Z+ovIonL5HCkTN8w6IakG/lsHUw/F5a/0rp8xI9g9HVQslVOqpWMVGOziOQv\nraQs3YI7/PMmeP7q1uX7nAQn/Sk0vDqHEl3d+p/B6pmPAp+HC919QQLH/hR4F9g+eH8F8Hd3v87M\nrgjeX25m+wBnAOXALsDzZra3uzcBfwImAPMIJcmjgdmEEup6d9/TzM4AfguMDRLxycAIwIEaM3sy\nnIyLSPpEJ5eZGI6U6RsG3ZB0A+7wrzth9mWty3uVwTmzoN++ualXalKJzSKSp7SSsnRpn6yBaafC\nRwtbl5/+AOzz3dzUKYZEk+Rhwe8pEWUOHNneQWY2ADgeuAYILzByInBE8Pp+4EXg8qD8EXffDCw1\ns8XAQWa2DNje3ecG5/wzcBKhJPlE4OrgXDOAP1po5ZJjgefcfUNwzHOEEuuHE7xeEUlAtpLLTN8w\n6IakC1u/CB48GTauaF1+1GQ49GdQVJSbeqVHp2IzaJSXSD7TSsrSJf17Ojw+vnXZgAPhjIezump1\nohJ9BNTITp7/JuAXtF5cZGd3XxO8/hDYOXi9KzA3Yr/aoGxL8Dq6PHzMyqCOjWb2MVAWWR7jGBFJ\nk2wll5m+YdANSRfT1Ah/vxpevaV1ef9hcMZD0KdrhIMUYjNolJdI3tJKytJlfLIabhjatvy438FB\n50PwVIZ81NEjoA4G7gC+DrwF/Mjd303kxGY2Bljr7jVmdkSsfYIWZ4+1LRvM7HzgfIDdd989V9UQ\nSatszq3NVnLZ3g1DqtcbPn7SmHLqNzXohqSQLX8N7j2OUP4V4eS7YL/TclKlTEglNgfHa5SXSJ7T\nSspS0F74Nfzj+rblP1kAZV/Pfn06oaOe5KnApcA/gO8S6hk+NsFzHwp818y+A2wNbG9mDwIfmVl/\nd19jZv2BtcH+q4DdIo4fEJStCl5Hl0ceU2tmJUAfQkO7VvFVsA8f82J0Bd39DkI3GowYMSJnybpI\nRxJNBLM9tzabrd2xbhhSvV7NRe4CNn8Ksy6Gd/63dfnQE+DEW2Hr7WMfV9hSic2Q56O81IAtkn1a\nvFJS1rAJru0fe9tV66CkZ3brk6KOkuQid38ueD09mJeUEHe/ErgSIOhJvtTdzzaz64FxwHXB71nB\nIU8CD5nZDYSGdO0F/CtYwfMTM6siNKTrHOCWiGPGAa8BpwIvBL3TzwDXmln4//JR4bqIFJpkErlc\nzK3NZWt3qterucgFbOFMmPGjtuXn/hUGfSv79cmuTsfmQhjlpQZsSQclfYlTg7Gk5K0ZMPO8tuWj\nr4OqC7NfnzTpKEnua2Ynx3vv7o934jOvAx4zs/OA5cDpwbneNrPHgHeARmBiMOcJ4CK+WhxkdvAD\ncDfwQDD8awOheVO4+wYz+xUwP9hvSnh4l0ihSSaR625za1O93kx8X7oxy6BP1sCjZ8GqmtblVRPh\nmP+B4h65qVf2pRKb836Ul0iqlPQlRw3GkrTmZvjNANjyedttv1gKvXbIfp3SrKMk+SXghDjvHUgo\nSXb3FwkCobvXAUfF2e8aQnOkosurgYoY5V8CMSeaufs9wD2J1E8knyWTyHWVxT4STTRTvd7KgaVM\nGlPO7IVrOK6if8rfl27MWktLg4E7/PMP8Pzk1uV9doOzZ8JOQ1KvaOHpdGzWKC/pDpT0Jae7NbBL\nCmqr4a4Yadyws+GkqdmvTwa1myS7+w+zVRERiS3ZRLDQF/tINtFM5Xprltcz5am3aWhsZv6yDQzp\n1zul7043Zl9JucHgo7fhzyfC5+tal4++Dg7+cV6viJlpGYrNGuUlXYaSvuR0lQZ2yaA/nwRL5rQt\nnzgfdto7+/XJgoQeAWVmOwPXAru4+3HBIyEOcfe7M1o7EQHyJ/HNxlDibCaa6f4s3Zh9pVPfbeNm\neOaXMP+u1uW7fxNOvx+2+1rmKlyAUo3NGuUlXZWSvuTly32G5JGPa+HG8rblX9sHLnot+/XJsoSS\nZEItxfcC/zd4/x/gUUKtxSLSBUUnxNkaSpzNRDPdn6Ubs68k9d1+MAceOKlt+dgHQ6tUSzz3odgs\nEpOSPkmU1hKJ8vz/wCs3tC0/+3HYM2ZbapeUaJK8o7s/Fl5BM3ikQ1NHB4lIa4XyhzhWQpytHt5M\nJprR338mPks3ZiEdfrdf1MPjF8CiZ1qX7zcWxtwIPbfNXmULl2KziCSsUO5BsklriQQaPodrd4m9\n7b/Xd6eFMVskmiR/bmZlhBYEIVio4+OM1UqkCyqkP8SxEuJs9vAmk2gmEvRrltczc0EtM2pqaWxq\n/f0rqc2cmN/t6w/CrImty0q2hnFPwW4HZq9yXYNis4gkJPoeZNKYcuo3NXT7hLnbryXy5iPwxAVt\ny4+7Hg4+P/v1ySOJJsmXEFqt8utm9k9gJ0IrVopIoKNkrZD+EMdKiPNxKHEiDQ/hfTZvaSb8wNXN\nW5qZuaA2L66hW6hfDg+fAWvfaV3+7cvgiCuhqDg39Sp8is0ikpDIe5CGLc1MmrWQZve8b7TPtG65\nlkhzE0yJ84imy5fBNt3zv4VoCSXJ7r7AzA4HhgAGvO/uWzJaM5ECkkiyVkh/iOMlxPnW65pIw0N4\nH48oc2BGTS2nDB+QV9fTpTQ3wz9+By/+pnX5jnvD9x+FHQbnpl5diGKziCQq8h7EzGh2L4hG+0zL\nxw6AjFk4E2b8qG155Q/hhJuyX588126SbGYnx9m0t5nh7gk9J1mkq0skWSu0P8T5lhDHkkjDQ+Q+\nAE1BttzU1L1vDDJm9etw3wnQ8Gnr8hP+AMPHdetHN6WLYrOIJCvyHqS0V0+mPPV2QTTaZ0Mh3O+k\n5Oo+scsvroEd98xuXQpIRz3J7S0r6oACsQiJ9xIXwh/iQlrYI5GGB90YZMGWL+CvP4c3prUu//qR\ncMrd0CvOsC7pLMVmEUla5D3IkH69CybW54tCuj/iw7fgtm/F3na1lq5IhLl7x3t1AyNGjPDq6upc\nV0MyIFt/1Arqj2ccyS4uVojXXIh1zlvv/w0eHtu2/KyZsNfR2a9PnjGzGncfket6FDLFZhHJBwWz\n+Oqt34S1b7ct/+4fYfgPsl+fPJRobE504S7M7HigHNg6XObuUzpXPZHsyOYftULoJe5IMouLFUzA\niNIV/p1y6vP1MOOHsPQfrcsrz4XRv4UeW8c8TDJDsVlEJPPyevHVTRvgd3vE3nbVOijpmd36dBEJ\nJclmdhvQCxgJ3EVo9cx/ZbBeImmR13/U8lAyi4vpu+1G3GH+XfD0pa3LtymFc2ZB//1zU69uTrFZ\nRCQ78nLx1dmXw7zb2pZXnhtaB0RSkmhP8jfdfT8z+7e7/4+Z/R6YncmKiaRDXv5Ry2PJLC5WaN+t\nhll3wn+ehYdOa1t+5FXwrZ9DUVH26ySRFJtFRLIgbxZfbWqEX8W537p0EWz3tezWpwtLNEn+Ivi9\nycx2ATYA/TNTJZH0yZs/agUk0eHIiXy32UxM2/usQh0anhNbvoRrdm5b/rV94Kzp0GdA9usk8Sg2\ni4hkSU6na/37MXh8Qtvy0kHw0zezXp3uINEk+Skz6wv8DqgJyu7KTJVE0ktzUDOnve82m4lpR581\nd0kdm7eEnpXcsEVDw2P65x/guUlty79+FPxAiyXnKcVmEZGuLN7jmybMgV2HZ7cu3UxHz0k+EFjp\n7r8K3m8HvAW8B9yY+eqJdB9dbTjw4wtqWxLTTM9Z7mh+dGmvnoTX8W8O3gvw6Ufw+71jb/vZQui7\nW3brIwlRbBYR6cJWvwF3HB57mx7flDUd9STfDhwNYGbfBq4DfgIMA+4gtEiIiKSoqw0Hrllez/Tq\nlS2JaXFxZucsVw0uo6Q4ND861mfVb2qgyKDZochC77u1x8bBO//btvzwy2HkL7NfH0mWYrOISFdz\nSyXULW5bftJtMOzM7Nenm+soSS529w3B67HAHe4+E5hpZm9ktmoi3Ue6V4rOda/03CV1NDaHUmQD\nTq0ckNF6vP/hpzQ1hXqtifHs91wuMpbrf4sW7bVM/3I19Nw2u/WRVCg2i4h0BZ+vh+u/Hnvbf6+H\n4h7ZrY+06DBJNrMSd28EjgLOT+JYEUlQOpO4fOiVjr6eU4YnvthTskllzfJ6Js1aSFOQGzc2eZtG\nhlwt4Jbzf4vmZvj9EPh8bdttp90P5Sdlry6STorNIiKF7Kn/gup72pYfOAGO/3/Zr4+00VEwfRh4\nyczWE1pF82UAM9sT0KB4kTRJZxIXr1e6M8lnZ+vT2evpTFI5d0kdzRG9x0VFRtXgsjb1z8UCbjl7\nlvTCmTDjR23L++wOP/s3mGW+DpJJis0iIoWmaQv8asfY2y77ALaNs01yot0k2d2vMbO/E3qkxLPu\nLXeiRYTmP4lImqQriYvVK51s8pmOHtDOXE9nksrw9TY0NlNkxpQTKwBy3pseWbesDPPe/Bn8ZtfY\n2y54Gfrvl7nPlqxSbBYRyb2EOxNenwazLmpbvtM3YOK8zFVQUtLhsCx3nxuj7D+ZqY6IpCpWL+7U\nOYuTSj5z1QPamaQyHdebKVkZ5v33X8HLMYZm7XsanKKnAXVVis0iIrmTUGdCvMc3XfAP6L9/5isp\nKdHcJZEsyPbiTdG9uMkmn7la6KqzSWWq15tJGRnmvXEF3LRv7G2XLoLtvpbezxMRESCPFmPMM93t\ne4nbmVBbA3cdGfsgPb6poChJFsmwnC/eRPLJZzYXuspUYD15+AAs+N1lAvZ9Y2DZy23LR10D37w4\n+/UREelG8iGeZ1pnYnJ3+F6iRTfGj6/+Lry0qu2Op9wN++qpfIVISbJIijoKKDlbvClKez2asa4h\nGwtdRQfWSWPKmfLU2ykF2uhznhy1snbBtXb/+zF4fELsbVethZKtslsfEZFuKl/ieaZ0Ntnt6t9L\nLJUDS3n0+4PZ/9GDQgWfR+3w33VQrDSrkOlfTyQFiQSUfBr6G0suW4CjA+vshWtSDrTtBeuCae1u\nbwXMs2fCnkdntz4iIt1Eew2p+R7PU9XZZLerfy9tzJoIrz9Im1nFh1wMx16TixpJBihJFklBIgEl\n3UOXH5q3gtkL13BcRX++f/DuQPygnkivabZbgCPrVNqrJ0Vm4E6PkiKOq+jPvCV1bGlyioNHObV3\nfKx6thes8761+68/h/lxFtuavFGPbhIRyaCOGlKzORUpWekYJdXZZDf8vcxcUEuXjVJbvoRrdo69\n7RdLodcO2a2PZJySZJEUJBpQ0jV0+aF5K/jlE28B8PKi9QAM6de7TVAHeHxBLdOrV9LY7O32mmaz\nBTjyBqSkyMCMZneKioxJY8oZ0q93kAh6zIQw1g0M0OZ5yJPGlLc0JOTrgl4tPv0Ifr937G0/egZ2\nr8pufUREuqlEG77zKTmG9I2SSrUR4PEFtTQ0NjNzQW3+jtRK1vNXwys3ti3vty/8+JWsV0eyR0my\nSAfaa53Ndqvy7IVr2ryv39TQKqjPXFDL4wtq2bylmfDDU9vrNU32GhJtrY61X6sbkCbHgxq6O2+v\n/pj6TQ00NoXq3dTUts7RNzDha41OmsPzmucv28CQfr1bzbPOm16Am/aDjcvblvfZHf7rrezXR0Sk\nm8vLhtQEpHOUVGcbAfJ+pFay4j2+6cJXYefy7NZFckJJskgM4QSvtFfPDheSymar8nEV/Vt6kMPv\nh/Tr3SqoG9DQ+FWCbNBhsE/0GhJtrY63X/gGJBxIwxyYXr2Sq79bQUlx6FqKi9vWOfoGJnytkUGZ\nGGXRw+VyFrhXzIN7RsXe9vP3oXe/7NZHRERa5FVDahLyIbnPdB2ysujm+7Ph4TNib9Pjm7odJcki\nUSITvCIzmppD/Z350DIanoMcPSc5MqgDzFxQ25Jonlo5gFPS8BikmuX13PT8fxJqKY7Xohy+AZny\nl7d5s7Z1wGlqDvUm40H27N7mvNE3MJHXGhmUc32z0Io7/E/f2NtGnAdjbshufUREJK58HE7dkXxI\n7tNdh8ikGMjsopvxeo2P/Q0cclH6PkcKipJkkSiRCR6EFpDyYGGpnCdchBLlcHIcFh3U2wtUqTwD\nMTyEu8ja751ur0W5cmApFbv2aZMkFxcX4UBj0CjR1OwJzQeLda2JBuqMtkz/6054+tLY265aByU9\n0/t5IiLSbaWS3KcrFqargSF6NNopwwekfyj3hiVw8wGxt+nxTUIGk2Qz2xr4B7BV8Dkz3H2yme0A\nPAoMApYBp7t7fXDMlcB5QBPwf9z9maC8ErgP2AZ4Gvipu7uZbQX8GagE6oCx7r4sOGYccFVQnV+7\n+/2ZulbpWqITvEljyqnf1JDV1tlUA1a8QJXqMxAdKAIO3XNHfnb03nGPjdWiHHlNJw8fwPSaUA9w\nS3+xOxW79Em6FzjWtSYSqDPyOKjNn8JvBsTe9r07YP+xqZ1fREQkjfLx0YjRo9GcNI4Qu6US6ha3\nLd9rFJw1vfPnlS4nk80km4Ej3f0zM+sBvGJms4GTgb+7+3VmdgVwBXC5me0DnAGUA7sAz5vZ3u7e\nBPwJmADMI5QkjwZmE0qo6919TzM7A/gtMDZIxCcDIwhNd6wxsyfDybhIe3I9bKmjgJVKAp2uZyC2\nlyCHRS7W9f6Hn7aZ2/3whCpuev4/vLJofUvPcf2mhqx992ldZOSGcvikNvY2zWMSEZE81VEsjL7n\nyMbc4Oh7jlOGh6aNdfpzGzbBtf1jb7vsA9h2x9QrLV1OxpJkd3fgs+Btj+DHgROBI4Ly+4EXgcuD\n8kfcfTOw1MwWAweZ2TJge3efC2BmfwZOIpQknwhcHZxrBvBHMzPgWOA5d98QHPMcocT64cxcrXQ1\nuZyT1F7ASrXFN9VnICYToDqa2z1x5J787Oi9mb9sQ6v6JPPdpxKsU15k5KN34E+HxN42/gUYUJnc\n+URERLKsvVgYfc8xaUx5h4uZpkO8e46kP+uZ/wuv/TH2NjVgSwcyOuDezIqBGmBPYKq7zzOznd09\n/BybD4Hwk7l3BeZGHF4blG0JXkeXh49ZCeDujWb2MVAWWR7jGJG81l7ASrX3M5Ve8mQbDhKZ251K\nfR6at4JJsxbS7O0/B7q96+nUZ8db4AMUdEVEpKC0Fwuj7zlmL1yTtcc8dbqzor3FMs97HnY7MLWK\nSbeR0SQ5GCo9zMz6Ak+YWUXUdjeztkvYZomZnQ+cD7D77rt3sLdIdrQXsNLxiIVs9ZInOre7M/Wp\nWV7PpFkLaQyeI9WQQLCO1euc8Ge/Pg1mxVnh8tJFsN3Xkqq/SK5ovRCRwpfKKKpkYmF0HD+uon+b\n0V95450n4bEfxN6mBmzphKws3ebuG81sDqEhzx+ZWX93X2Nm/YG1wW6rgN0iDhsQlK0KXkeXRx5T\na2YlQB9CAXkVXw3pDh/zYox63QHcATBixIicJesi0eIFrFzPl05GJus6d0kdTREPWi4yazdYd2qY\nenMzTImzz9ATYOyDnam6SK5pvRCRApbKtKtkj40Vx4f0651f9yDxRnd95//BQROyWxfpUjK5uvVO\nwJYgQd4GOIZQoHwSGAdcF/yeFRzyJPCQmd1AKBDvBfzL3ZvM7BMzqyIUiM8Bbok4ZhzwGnAq8ELQ\niv0McK2Zhf/vHQVcmalrFQnLxoIW2egJ7uxjotprnU7nd1M1uIytehTRsKWZoiJjyokV7Z4zqWHq\nM8fDW3FWuJy0AYqKU6q7SC5pvRCRwpbKtKvOHBt9z5EXz5Fevwj+OCL2NsVpSZNM9iT3B+4P5iUX\nAY+5+1Nm9hrwmJmdBywHTgdw97fN7DHgHaARmBi0VANcxFdDumYHPwB3Aw8EQXsDodZu3H2Dmf0K\nmB/sNyUclEUyJR8fo9AZnbmORFbkTud3k2wvdYfD1D9fD9d/PfbBY26CET/sdF1F8k2+rxeiqVAi\n8aUy7SodU7Y6I22N5DdWwMcr25ZrdJdkQCZXt/430OYp3e5eBxwV55hrgGtilFcDFTHKvwROi3Ou\ne4B7kqu1SOel9ZFCWRYZwKKv4/EFtW2CW3TA6+jaM/HdJNOaXTmwlEljypm9cA3l/bdn7pK6UPm9\ng+IfpDlM0kXl+3ohmgol2ZSNEWDplOoCnJ05NtU50Ck1kjd8DtfuEnvbL5ZCrx2Sqo9IorIyJ1mk\nO8hVC22qYj3iIXwdxcVFTK9eSWPzVytIA20CXkfXnuvvpmZ5fctjK/p88Bf+2PMWeCnGjhe8DP33\ny2rdRHIlX9cLEcmUWM/8LcQRYKkMeU722GS/o2Qb0eN6aCz852+xt6kRW7JASbJImmRrUa1YQT6V\nz4wOYPWbGlquY9XGL3jkXytaBTegTcCbOHLPdq89G99Ne9/D3CV1vFd8BsSbptSJgFtovQ8ioPVC\npPuKlewV8giwbEnmO4r1HSfVSN7e45smzIFdh6fhikQSoyRZuo10JDUdnSOdC1rE+qxYvb7hHtLO\ntoLHCmDh66hZXs/jC2rbBLdYAa+ja8/kYh9xW7rvGAmrFzAxxjHfbLqdW8Yf26k6FWrvgwhaL0S6\nqVjJXnsJXK4bQnP9+WHJJLkzF9SyeUszTuKN6ADMux1m/yL2SdVrLDmiJFm6hXQkNdlMjGIlw2+v\n/piFqz5uFeRnL1yTcit4e7288bYl0iuczQAfefOzVeNncecab+jZnzuGPcHbaz7h4or+na6Xeh+k\nUGm9EOmu4jUIx4pnuW4I7ewCmonG3GT2TXQkWM3yembU1BJeRKC4yDpuRI/z+KYbG0/ldjs1dN3t\n1k4kc5QkS7eQjqQmm4lR5Gc1bGnmv2ctjHouMPQoKaK8//a89kEduMds4U00EEYHsOjjYg2f7mhO\nUjZvMKoGl7Fkq+/H3b5348M0NjklWwxeXUZjUzPzl21gSL/enapXrudYi4hIcuIle7HiWTbjfaw4\nneznJxNza5bXc+adc1vi18MTOo7PiYwEm7ukjsamZgAMOG3EbrGPWbUA7hwZ8xxTvz2f3z+3iGaH\nYlMDtOSWkmTpFtKR1ITP0bClGTOjtFfPNvukq/c0sr5mRmNEgmzAoXvuyHEV/Zny1Ns0u1NUZEwa\nU56Wxy5FHldSXMSplQM4ZfiApK4n3TcYcb/X1x+EWRNjtjS/2TyYExt+DYDhoeFfTQ7h1ynUK1Nz\nrPNleJ2ISHdWNbiMkuKvFrDMVENovDid7D1LMjH38QW1NDSGktmG4AkW6Yg30XU+efhX6/rVLK+P\n/zSJnfeFC18JnWN5PT3nfKAGaMkLSpKlW0hHUhN+jNCkWQtpdmfKU2+36olMZ+9pZH1Le/Xk6r+8\n3RLUehQbPzt671ZBEZz6TQ2tzhG5ffOWxANhq17sxmYemreCx+avZMqJFXz/4MSeWZrOntaY32sH\nj256aN4KfvnEWy1FxcWGNzvFRQZmNDWlXq90z7HO9fA+EZGuLOm/se6tf2dAvOQ22XuWZGJu9NWk\n6+pi1vmLevjtoNhDpi/7ALbdseNziOSIkmTpNtKR1NRvaqDZPWZrbbp7T6Pr++j8Fey8/dZccPjX\nqRxYyvsffkq4g7nZadOzHW4Jb2gMLaIxvXolJ0f1CMfquQwH2/DiGwCNzc6kWQsTHp6czkAX/l5b\nhlPfG2OnUdfANy9ueVu/qYEiC30vBpT3357yXftwStCyHa4XwNQ5i/MiGHf03496mUWkuwn/3Svt\n1fSL5qMAACAASURBVJP6TQ0pPds3mRg9d0kdjc2hUUdNzZ7QcOfO/H1uL7lN5p4lmXnDBpQUQVNz\nqNH9lOEDYu7bGS11vuc4WPFqzH2mHl7DxJF7dnwOkRxTkiyShPYCWrp7TyMTufAK1u9/9CkXHP51\nIJQIGqFWYAMWrm67AuTQfr35d+3HMQN9vFb1cLB9fEEtj/xrBU0tiXjHNwqROprnnBB3Jr5UycSt\n4myPs+ply9D44Ibo37Uf8/5Hn7YMGw+v3J1PPbcdrbKaT3UVEcm08N+9cINtkZHUIlbJPIooOj4l\nE89T+fucaoNyR+uHxKtnSXERpx+Y/FSqdrXz+KbvN13NvMa96VFSxDQNoZYCoSRZWlFvVfuih0GH\nnxscDk6TxpQze+Eajktw5eTwI5YcWoJVdMA9efiAmK3fVYPL6FFsNDSFWrtn1NS2OUfkzUV0oI8e\nVj3lL2+39LaGr6d8lz4tw8t7ppD4J30TEWfFSwD+6x3os2u7nxf+d7rp+f/wyqL1Mecg59sK1e3d\nLOVbXUVEMi38dy88oimZv3+x/mbGexRRvPiUaPKa6t/nzvacJhtXI+vZ1NTMrn23SU8cefUWePaq\n2NuChuyf695SCpCSZGnRnXurYiWr8YS3RX9X8FWPbyIrJ4dXmAzPNZ5RvZKHzz+kTcA14j+X+LQR\nu/HQvBWhnuKmr4Jz5M1FEaGFvn529N6t6hPd2/pm7ce8WftxSz0qB5by/YN3Z0i/3ikHt4RuIta+\nC7dWxT9Jks9KrBxYys+O3pv5yzZkvOc/XeLdLOVjXUWksBRaI3jkYpnNxG7s7ejYWHEz+trbmxec\nyPeUq7/PySbnaa9nvMbsoybDYZe0KtIQailESpKlRXftrYqXrCbbcgwk9f2FzxG2pclbbmCiV4g8\nefiAmDc3Jw8fwMwFtW2CXvQ5ohNkaN3b+vKi9W3qEe6RTvdq3W2Cc3u9xkkmxtHa692Pty0fbyS1\nmImIpKIQG8GjR24lOic5/Dd80pjyhI5JNXnM1d/nZOudlnqumAf3jIq9bfJGMEv+nCJ5SkmytOiu\nvVVzl9SxJUay2pmgmsz3F73QVnGxtQSuWMlbrPrEC3qJBMPwjcRxFf2Zt3RDq9WzqwaXtbmpSvSG\nI5Y29XntYrj3qbj71/xwWVpuNGqW18ft3Y+1DdqOEMiXG0m1xGdHPjaSyP9n793jo6jzfO/Pr7o7\ngWAIIVwChCREFDVxnEkQwowKjqMjs7o6oIJ4xpnZUfBZ9uzOzp6zOu7I8DCXx9ndZx/3nOUcRUad\nmTWAXBT1GbygAuJyTVaGRAxCSEIISSBpSCS37q7f+aMuqaquqq6+pW/f9+vlS/pSVb/q7tS3vrfP\nl4iWVA2Ch3vdcxoMMAqCRWPfIllnLIjE6dWuM6xrnVUwu+hm4LHd4S6dIFICcpIJlUh6atOB6rIC\neOSyY2DESbTDyjgZn7MzQsaRTQ/NnaluE07ZtpVx1vbeah8D+hsJgTE8dsss9A35deXm6z86NdKz\n7BN1vcmROI9VJfnS6Ka95q+XDdVA5ICLAT8J8wbO6nO2G4MVi2oAIr1IxWwbQTgh1YPgTh06J8EA\n7d+5MgEh2xPd33uigmtx7Wf+sgv452vMd/D3Z4CciRGsmCBSB3KSCZVwnbN0oaokH5ser3bck6zd\nzqyEOZQYiIIxk1wxXYrUxirib3d87TFEzvHix0147Ymvm/Ys+/wiGGOWo69CYldO/Z1/BuY9jtoW\nL7I2HgzrBk6bCVB+t8bzNI7B2ny4FeXT87BifnFMqgGI9CJVs20EEYpUbtkIJ3jlJBig/TsHYCrs\nGI/1GadWJPK7sL3W/Wo64LtivmGULVAEkUqQk0yoZPINYjxKpeyymIB+lq/ARjLLsYr4232f1WUF\n0HYOBTiw3bA+Yz/YurcbnK/JPwT8corly+sX1oZdHq7FmAkPyPMsjedZVZKPB6qKUHOoVT1P7bxn\nJ9UAROaQ6tk2grAjVVs2wrk3cWJLjKKVDABjLChwHcn6hh1kr90uAeAcfjHyyqxoCbrWzcq3Dmg/\nuhMoWzSayyOIpICcZEIlVW8QnZY5jXY5lDGLufXoWSypLAIA1fG0Ut+0M/JOz8Pu+6wqyccd10/F\ne591qs+ZyW1ob6ocqVzbZY2fbEZtl9zz+15j0M1BODdw+kwAh0tg4Jyb/m6XVhbhtSNn4ZfTBtp5\nz8p/tS1erP/olKNZk0T6ksrZNoJIVyIRqArVoqT8nfcN+LBx/xmInGPd2w0RVdDl52SpWWmRB1eJ\nAcGOPhB9BjsalM/A8+6T+Er7FuAVkzdR1pjIcMhJJlRS8QYxnDKnSHoNaw61qj3aK+YXh7U2JYu5\nSR7R5AtwvLD3NPZ9cSGkGJbRyDspLda+T9mf3fe5auHV2HPygk5FO9T5mH5mR18G3v6x9YYaQ3uw\n6VRMqhWMN012oitVJflYd1+F5bxn6kMltFCQhCCSi3jcmyh/5+s/OhV2K5HRznr7h8EAdeSiUW8E\n0Nssl5xJDojmgV0nRB30X5uHKrPnb/kJ8K2fh7+/UWS0Ex4k5pi5kJNM6Ei1G0RjmdNzu0+ajjuK\npJS85lArnn79OACoI5LCdZSXVhbhtaNn4Q9I5cC7T3SC85EIsrd/GKtvnx20nbF3SVtarGREh336\n87By9swypcBIL7bZxd/MKBjXVPVyqfWJW0Sgw80IWBknYym4nSppbYsX9e2X8c3rpmBSbnZQz3km\ntxkQBEGkAk7uTSJxZiKxSUY7W11WgGyP/T6Mjj4QeU9yxIHdk+8BNQ+av5Yi45uszj1ejiwF0TMb\ncpKJlMbYW7T/i4s40twTdCGLpJR8V/35oMfhOskAICp1WICq3sy5dQ+Usd/29uum6ES21H1BX9Zl\n7IF+fu9pfHXmBNvss9mNh5lRACRH/XPXckt1agAhy7PCyQiEMk7Kv+3eU9vixcMbDmA4IH1uWW4B\nSw0Z81RtMyAIgiAkInVmws1SmwVVV98+29E+jPY2VGA6nDXYbmPVBpU7Hfi7E7qnkj1rajWVIl6O\nLAXRMxtykomEEYuLsWLgntt9Evu/uGjZ4+PEEBrXs7himppBBoDFFdPCXt/Bpm5ofGR1LXWtl4J6\noJTS7rEel84p/vDzLggCgxjQ74hBX9ZVXVYAt8AwLGet3/+sE7s/6wSTxcFg8dmYrTnoJmBvFT53\nmb9//cJa02y4FU6rFXbUtWHIJ9r2bYUyYAebuuHTfG6R/jYIgiCI5CUaZ0Z5n9m4RCNWQdVoqvDC\ndfAdBXZ7zwP/cp35DizGN6VC1tTs3OPpyFIQPbMhJ5lICLG8GFeV5OPH37oWR5p7QpY7WR3DbD1K\n1jhUT7Kds6/NdANSFvmaqbk42uLVXdAbO/rU0m5AUrtWEEVuWgXlEvTznKtK8vHg3JmokXugAams\nW0k+C4Cji7yy5s9dy6UnTDLH/z3wl9jhvwUet4BXY2A0jJ9hbYsXW4+eVc/D5Qped22LF+2XBuAW\nmGVvV3VZATwupmaSI/ltEARBEMlNNM5MOPcj8Qiqhuvk2a5h7QQA3HzDEJVeqZA1tTr3eDmyFETP\nbMhJJhJCrC/G0V7IjOvZUdem7suuxNpJSfDae8vxzM56BEQOQWDo6huC2yUgEJAEPM5dGsDrdW26\n/ZZNGofm7n7JQZadQC0CAx67ZVbQeS6pLML2ujYM+0SImucZgG9cM8m0X1t7Lg2f1ePRQ/dYZo1n\nDdbg4fnFWF5ZhNIYGQ3jZ7jmnnLsqj+vZoAZgAeq9H3ExnEay+bNNJ1vXVWSj00rF2B7XRsYpM+H\njBxBEMToMFrlu1Ul+artWFwxLaxjOb0f0Z5LONVToYjEwdcFdsUAsC44MwwAn39nK66bd1fc1pEI\nzMrW4+nIUhA9cyEnmUgI8bgYR3MhMypPbj161tEMQ6v+GKPyJZfTub4Ax+7POuFxMdxx/VR8+Hmn\nqn6t5S9uKQMAbDnSiotfDuHcpUH1NQapfPqlT87gzvJCU0eZAcjNdkujLUSOLI9g6iArRn/13ipU\nAaZql9cO/R7D3A0BQLZHUJ3RePT8DPtFVYVaUQrN8gT3EWu3CQREzJgw1nEfGEEQBBF/RrN8t7bF\nq2pvHGnuCWuUU6j7kdoWL7bXtWFbbRv8gdgLRkXs5L36EPDFu6YvXRfYLH3ubwXw6lRvXPqzkwmy\n80Q8ICeZiJpIjESyXYy16zl3aQCbD7eqAljb69os16eWU/tEMMbwRWcf/r/3TyIgcmR7RpQvs9yC\n2l/LAQREjs7eQfjF4H3eecNUzCnMxcMvHlTLtAHJOdb2Fw8HuG5tNYdasUbOWLsEhnX3VWDLqgWW\nn/HlF+9D1bk95mMgAPzDTfuxSf4cBAZ8Y7Z9JtoOJyXpPr/0GQZE2UG2OWaqRLwJgiAyldEs3422\nJ9l4P6I4xhf7hrCnsQs+WesDiI9gVFhOnpUQ16KfAouewvqPTmH4vcaIP4tobHyoaRMEkUqQk0xE\nRTSR4mSL/CnrqW3xYtvRs6oA1tajZ01LeZVt1txTrjqnb3zarr425NMrX+6oa8PWo2fV/tlstxC0\nP4EBV08aJwlOGRzkW66ZhLEeF977rFP3PCB9D2t21qvjofwix5qd9diyakFwWZhsYM3MbNlQDUQu\n7ddz9KxaFu5xm2eineCkJF07zmnd2w2q82t1zHCCLMmu1kkQBJGOjGYw0+mx7MYKKo+NUxG0MCDm\nglGObNSnm4A3njB/zTC+abSDyIqNH/RJ9ywMUJMEZHOJVIacZCIqUkHoIVyqSvKxaM4U1Rn1BTh2\n2GSTG9ovq9lPLRz6EU3TJ4zF2j+vUKOs2+vacLjZq9tG5MDz+5rwxG1l8GgEvzwuhh9/61oAwJ6T\nF1Tjt0QuQ5ZUtPUrEEU+8n1YRZ5lSgdrMK80H8V9Q2ju7lez3cvmzcSMCWOjcjCd/Ea0NyhzCnMd\nObVOgiypoNZJEASRjoxmMFM5lqI/YXUMJ/bAOBVBwe1iWDZ3pk7bIlxntLbFix11beCA2kZkuyZL\n282AtZdMXxntSr2DTd0Y8o0E9Tmk1qlI7gcpqE0kE+QkE1GRrmWvRvN4qKkbNYdag8qIjCrMRrz9\nw7aGWSnrNtJwvhebHq/WGVNlm7X3BouTGEu6GaRe3tV7qyznGpcO1qj/dgnAp2cvqZloRQnbKoMe\nDuH+RuLV75wuQRyCIIhUYbSDmTvq2jDsl9qkjPtxag+MUxEUBOjFH8N1RmtbvLo2qm1Hz+LBuTPV\nNantXTkXgPU3m+/EYnyTkdGs1MvPyQJjI5M0AEBgLOz7QQpqE8kGOclEVCRbb7Ed2gglANs1T8nN\n1j0+deEKnn79eFAZkVXEGZCizsaSLG2Pc1VJPuZMzcWJjr6gbRUHWCsMomAmTqL9HlbvteoyBk5+\n+99xVLgJP3vjuO75iul5OH7uckz6j40k8jeSrkEcgiCIdCFWwUyr/Wj7ZZ3Yg6qSfKz98wr87I3j\nuiB2QFudpXmv07Ua26iUPme3IDnkZ8asAI5B+s9IiPFNiUIRTNM6yG5ZEyXc75CC2kSyQU4yETXJ\n1ltshnFkEDi3VK+ubZFKoN0uBr/BAebQX7z7BnymWWQGYNncmep+FSPIAWyrbUPF9DzsaewKcpCL\nJozBPV+Zjob2y3j69eOomJ6nOsVZcmZXyRYP+UQ8t/uk5Mz6alH170stRbhKB2vgYsDyjmJsrxsx\naIrTv+zmYjR2hu4FjpRE/UZSKYhDEASRicQqmGm2H7MRg06EpfY0dukcZG0vcqRUlxUEtVE9cNMU\n/PrYLYDHZIPvvwXMui3i440GimMbSmzTCXa/AyrDJhIBOclERmCMUALBDi8Q7ExPHOdGzxWfbl/a\ni3fD+V7da4xJO/a4BZRPz8P6j06huqwAD86diVcPtQLyMbUiW1oKrsrGbz85o2anXbKatbLWrr4h\n1SnnAP5w9i7gZfNz1pZTS8rY0oxmK4Nm7AVOF6OUCkEcgiCIeJAK13EnwUwn52G2n/UfndLZfm//\ncMgZx7UtXnzweZfuua8U5WHNveVRfYZVJflqG9VPG+7FVX4v8HuT4/+wOWm/KyNGxzaaALvV74DK\nsIlEQU4ykfTEwsgb5yCDc1VlWhutNM7fnT05D4evjIhr3Vyaj6cWX6+uY3HFNHz8xUX1dQZAhLTt\n2rca1JmKP1hQqr6HA6YOMgBkuwVd+XaAS1lozqW1TsnNRvOYFbbnqihUK3hc0lglv8ixp7ELboGp\n5641aEZ1z2UbDsAf4HC7GLasXDAqRikVbugIgiBSgVRyLuyCmeGch3E/4WSpFfvTfmkAXFM/7GKI\n2kFW1/dyqWnF18VrHsSW6U+lXJA61tVaZr8DKsMmEgU5yURSEysjb7yQA+Y9yfk5WRDkdLDHLWD2\n1FwcafaqYliL5kzROZLe/mE8cVsZGs734mLfkFo+HeBAQM5Y+/wiGs73Qk4ygwFwCQyiyKF0JzEA\nq24rQ++QX6d47WLAY7fMQsP5XilrbNarhJHIc22LF27N6AqXwPC1mRPUffoCHLOnXIV5sybainL9\nZtcJtdTcH+B4ctsx/OaBm+JqmGLxXafKjQVBEES8SRfnwkrXwwmhnDhtv7LS2uQWGNwuybFmDHj8\n1rLoPrd9/wR8+Evz1+Re40kAVmvW9PCGA/AFODwuhk2jFKSOlHhXa5G2CJEo4uYkM8ZmQiokmQrJ\nN9jAOf9XxthEAFsAlAJoBvAQ59wrb/NTAD8CEADw15zzd+XnqwC8AmAsgD8C+BvOOWeMZcvHqALQ\nDWAZ57xZ3ub7AH4mL+eXnPPfxetcifgRSyNvvJCbGct1bzcgIHIIAsOt10xGxfQ8eEwuzkaH7gcL\nSvH8F01Bx1RUohdXTMOR5h51P0pfVH5Olq4/SpnR7AtwCAw4nb0COGx+PvViKf5hynpdhLuqJB8P\nzp2JmkOtUlk25xjSCIUAwKmuL9Hm7VfHTxipbfHiiGE01akLV/DIxoNxzURE+12nUtaEIAgi3qSL\nc1FdVhCk67FUM/4w0nGBWpshMKaOcgyIHN+8fgo++rwLAZHjlQPNuLO80DbTbboOu9GLNkJc2+va\n1ED3cIBj3VsNMctkOyHZgs2kLUIkinhmkv0A/o5zXscYywVQyxh7H8APAHzAOX+WMfYUgKcAPMkY\nuwHAcgDlAKYD2M0Yu5ZzHgDwvwE8DuAQJCf5bgC7IDnUXs75bMbYcgC/AbBMdsR/DmAuJAe9ljH2\npuKMx5Nku7jEg9E8x1ga+VDr1gpQBESO9z/rxEeNXSNzDTTlV8bI9vP7gh1kBuAb14TX81tVko/N\nP6pE5e/nWJ6Httd4xYy8INExDsDlYggEOFwCw7Kbi3HifL1unIWdE3qwqdtUjCxSx9XpbyXa7zpd\nsiYEQRCxIF2cC2PwNxCQssnKuKdIg6JamwFI9lLb2iRybqpdosUYnN3x3Vzc8OafmR/Q4fgm45zn\nP7VdjnuQWiFZg82kLUIkgrg5yZzz8wDOy//uY4ydADADwH0AFslv+x2APQCelJ/fzDkfAnCGMXYK\nwDzGWDOA8ZzzgwDAGPs9gPshOcn3AVgr72sbgH9jjDEA3wbwPue8R97mfUiO9aZ4nS+QvBeXWDLa\n5xgrI+9k3cZZwwB06tba8Q/Ke4d9IvR52hEYk3qWlfFNxp7foPW8XAoAqDTZ161Dz+Esn6J7zi1A\nlw1W9qldv7L8TSsXYHtdG7bVtiEQsHdCq8sKgpS9lYx4OI5ruL+VaL/rdMmaEARBxIp0cS6WVBZh\ne12ben1nQFBQFAidWdYGbo02Q6t8DUB3PCt7ojjaTdmyVsibJm8Kc3zTksoibK1tU1WwQznqsYSC\nzQQxwqj0JDPGSgF8DVImeKrsQANAB6RybEByoA9qNmuTn/PJ/zY+r2xzFgA4537G2GUABdrnTbbR\nrmslgJUAUFxcHNG5acmEi0sizjEWRj7U/ETFqL76WDXWvdWAY20jRs0lh3W1IyW217Xhtmsmo7N3\nEH9qu2yaeR2X5dKNb9I6icp6/quwHX/r2m6pUH2tbxP+4huzcO7j4Ez1spuLdZ+LNhOuEBA51uys\nx5ZVC7C0sggX+obQ1Tuobusk0ytlo2fa9jCbEclvJZrvOl2yJgRBEOlGtBVoZroiWic2PycrZFDW\nLHBrZzNC2hPfIFbvrcLqbJMF/5ftwOxvhX2eyrkqKthbj541FRmNFxRsJogR4u4kM8auArAdwI85\n571SoldC7is2l/kdBTjnGwBsAIC5c+dGvY5MuLik6jk6mZ+oGNU195bjoRcOICBK5Ve/uK9CF11+\nWCOM5XYxuF1Mp0itcP208Tja4oXIpYj3urcaUD4jD0sri6wNK4Cnb9qPGnlcFMBxoKlbW+kNQHLc\ny6fr+52sstsi56qxVdZ9oqMBAEyd+B11bbossihyzJgwNiUyu+mSNSGIeEF6IcRoE0sBTu12WifW\nSSDc7D2rb5/tWClb5VfTAF+/+SLDzBpboRx7SWXRqAZ+KdhMECPE1UlmjHkgOcivcs53yE93Msam\ncc7PM8amAVCG0Z0DMFOzeZH83Dn538bntdu0McbcAPIgGeRzGCnpVrbZE6PTsiQTLi5VJflYc085\ndtWfx+KKaTE/x3j1O5t9N8b5iYpRbezoQ0CeoaT8XzGw5y4N6Ec0Bbiuf0hgwJTcbNz/1RkoLhiH\nutZL4JxD5MDOi38GXISlQnXpYA3uumEq0Deke37q+DHwuHp1PcUBDvz8zXrMKczV9TMr59g34MPG\n/Wcgco4styCVaxl6knfVnzctV9t69Kzu+JE6uJnw90AQKUhG6oUQiSNeFWhGJ1Y75vHcpQHUHGrV\nKVYvmjMFbpeAQGDkPbUtXudrsRDies2/ED8NrMJP7pqjKlTHikQEfinYTBAS8VS3ZgB+C+AE5/xf\nNC+9CeD7AJ6V/79T83wNY+xfIBniawAc5pwHGGO9jLFqSIb4UQD/07CvAwAeAPChHMV+F8CvGWPK\nX/ldAH4ap1PVke4XF0UBetgv4khzj85JU16P1CmKd7+zsSe4/dIA3ILUe8sYQ35OFgBgV/153Xb/\nz64TuDLkh8ilzLFLABTBaEGeOazAOfC9BaWoLivAIxsPIiBynLGZa/xQ4S7dyKfdJzohaLxuj4uh\nbNI4vP9ZcKbaF+D4za4TWDhnStDnfWd5Ie4sL9SVpm3TZJLNFLeVQIB2hvNNRXlRqWqm+98DQaQa\nmagXAmSGqGayMhoCnEpQVqma2ny4VadYPRzgeO+zTrgF4I7rp2JPYxc2H27Fjro2y3uN2hYv+Ae/\nwNzW35qv5YfNeGTjQfhC6HwQow/9vROxIJ6Z5G8A+B6A44yxT+XnnobkHL/GGPsRgBYADwEA57yB\nMfYagM8gRbpXy5FqAPhLjJR07ZL/AyQn/A+y0e6BFO0G57yHMfYLAEfk961TjDIRHXYR4Wid3NHq\nd6451Io1O+shcqmcmjEpY/zMznp81NiFgnFZuvf3DfrVf/sDXMr2AujsHcSsSePwxqft6utuF5Mu\nyi+X4nMXAFfw8Tf4/wy/9j+CGRPG4Jxh1JLIAcVHZQBKJuZgw74m055nADjS7MXRFi+yZNERbfn0\nmnvK1fdVleSr4l0MkjCImeI2AL2QySiOnRhtyIgSmU6y6oXEmkwQ1UxmRkuAs6okXw30ihzgxj4l\nSAHuzt5B9T2W9xpr81BltRC5pLoKDvqWYwDZqvCgv3ciVsRT3Xo/gpXsFe6w2OZXAH5l8vxRABUm\nzw8CeNBiXy8BeMnpegln2EWEDzZ1q8rKw77wndzR6GGtOdSKn71xXHVEuZxZ1Y59csK+Ly5g2C+i\nob0XTN6+EN046PmvliJc2tFNAHDu0qDl/pV9nrpwxXYdHFL2etgnYsO+07rP/5md9RBlwY9Nj1eb\nZnWNz2VKiTQZUSLTSWa9EBLVTD9iUVW0va5NtXFW36OqzaGOdgpm6vgxaOzsC77XaKsFNn7TdJsb\nBzeiDzn49XdvhLY2zFih5lRdOz8nS9U6scpiK++zEgC12/9o2u9kc+Lp752IFaOibk2kD3ZOVH5O\nlprxFOXHsdp3LKht8eIZjYMMSGOaGEZGJYXCJUiOqWKoRc7RbFNOfd3gyxhEtmW0yAwGoKQgB609\n/ZZGHgAm5njQ7wuoQl0t3f3gkMY1MU0Z+LBfxI66NsefZyaUSJMRJTKZZNcLIVHN9MTKmXLqXG6r\nbVPvMVwCM/0elfuI53afxMdfXDTd16I5U7Bq4dUjx5THL5phDG7vqj+PFfODAze1LV48vOEAfAEO\nj4th08oFturaIpdsfbYn2PHVvk9bMh7KViUi+JuMAWf6eydiBTnJMSbZImrxwMqJami/bPs4nH3X\ntnix/qNTMf0cf7PrRJAzLHLgtmsm4ZPT3breYiuunZKLPY1deC1rLW4WTlq+r3SwBgKDatCV/9//\n1ekYl+3GlqNnEdBksbVwSOJf7ZcHVYGRhddOxpmLV3Cq60v1fT39Ptz/1en49OylEQeZAd+YPQlj\nPC5dVjxhKaEkhYwokalkol5IplTIJDNmzhQgZYe31bbBH5AcwnX3VZg6oQebuuEPjMxtWDRniq0q\n9Y+/dS0OnelRZw0rCAC8/cOompaFqpergL3B2z8aeAZ/89iP0NjRB8EQWF9cMc30Pm97XZuq+TEc\n4Hh+72l8deYE3Xu0wVnAev6x/n0cApPuVawCA2bbZfJcZfp7J2IFOckxJBkjaqOJmbMXCfH4HGsO\nteoEsrTs++IinritDH1DftSfu2w59xgAdl26F/CYv2aMOI/xuDB+jBsdvSNq1d1XhvG9BaXS7EOM\nfEYM+s/rcLMXLiYJjKxaeLUaOHjg+f/QjYN685jUD63dtnzaeNxZXoi9jV1qVHtppTbhQ8RbpZ0g\nkpiM1AvJhAqZZMboTO2oa9OVTwNSZdYzO+tR334ZS2XdDIX8nCwwxqAYwA9OdKLmUKvqUBsdHQjT\ndAAAIABJREFU16qSkVnDXX1D2HvyAgIBEaezV0iOsYlzXDZUIzmjDJgvj4eaU5iLF/aeRmfvIJbd\nXIw5hbmm9yfGarEPP+/CByc6de/Jz8mCwBg452rVl1mQVhvEdQlMqloLcKn0zYZEBH+TNeBMf+9E\nLCAnOYYkY0RtNKkwzO01PnaK088xnKy9UbHayIsfN+G1J76OJZVFeGTjQQz6RqLPduXUQLBzrNA/\nHMDAcED33OKKaaqwiNaxNXPKA1wytKsWXq2e600z8vBp20iGXivyxeXHz+9rQnHBOGxaucBxf1Sm\nRVtDqbQTRLpCeiFEIjA6UxxSK5DR9gVEjk2H9KrTyvVaW+0V4MAzbxxHQ/tllE/Pw9o364NKnbUZ\n3Beb7jC/4523EvjOP6G2xYusjQfV9eXnZKnVbBsenau+3Wp05JLKImytbYPPL0IQGERDiXRjRx/W\n7KxHQJQEQx+7ZRZyx3pMba82E9p+aQCbDrdKuikB+/vKRGRQUzFrm6n3PUT4kJMcQ5I1ojZaePuH\n1bIggUmPI6G6rABu18i8Q7PPMdxsc/m08Zb9SYC05u11bZgxYSzuLi/EG5+22zrHVo6xEcWkCwxY\neWsZVswvRs2hVtteY/26OLbXtWFHXVtQ2ZiyX8VB1maYtxxpxV3lhSEd5ERWPiTSUGV6QIsgCGI0\nMTpTALBNdioZg84mGsuQleu1kQCXqsS0oxiVUucXH52Lzq0/QVXDb01Vqmt/2Dxif6CvLiqfNt5S\nLMvqPk/JXGvFtrQO95qd9ep4RZFz5I71YPXts20/LyVAsL2uzfF9ZSIyqKmUtU30fQ+RWpCTHENS\nMaIWS7TOrdvg3IbtECken8kIB8CZk6M9Zu5YT1BJsxaXIBnsk+7lAIDnxgS/54nhH+MdcZ7uOSUd\nE8rnZQByx0p12k6DBwKTxjExQCf0oWXlrWXIHetB34APz+9rUp9vON+L4+cu2xqBUOO84vk7TrSh\nyvSAFkEQxGhjVIJW7DuDvuWIQV+GrL1eM4GBc6iZWg7p31pebLoDWDsyw0xLzeLj2HKkFZ9tOICA\nyCEwKavbN+TH1qNn4Rc5DpzuhsjNR0TZ3edpz087XvFgk17zRGD2vcXGzyzc+8po7He6Z1kpQE6E\nAznJMSaVImpmRH2BNHFu7Rwis3EI2nLkgMhtxzyYlUYp0VftMdfcU45sj6Drf1K4c9wpvBhYY3lK\ndlljl8DU6LCWCWPduCrbjY7eQXCuN/ihVL9dAnDHdVMxOTcbS+ReYiWS7HIJCIgiAiLgcTHcWV6o\nfjbFBeOw5Ugrrgz50XTxSkgjYOUoRurAhvPbSbShyvSAFkEQ6UOqOTa1LV48t/ukauc5hy4bLDBg\nzT3lpo5pfk4WGtov6/qMBYHhq+IJbM1eZ3q8GwZfQj/G4KtFefj09eO610TOdQFmQFqQIDAwcNMg\nqhOBUeO9YLZHwLBPWutjt8zCwaZu9X2hCOe+MpoAdKKD16MBBciJcCAnmVCJ9gJ5sKkbvoBk9PyB\nEefWyiGyGoew5p5yWwcYCDaaSmmUoo7p7R/WHdPbP4w195Rjy5FWVZhLLacOBJ9L6eCruCrbjS+H\nTF6UcTHgnq9Mwxuftge9dmnAj95BP9wuAQ9UFaFiep5qFO1Uv2dPuQqtPf3YLQt+LJHFS5QysLEe\nF3afkFSrRUMAYU5hLho7+9RAgMDMRUEUrBzFSBzYcH87yWCoUj2gRRAEMZqOTShn3Okop0c2HtTZ\nqSy3gBtn5OGIRlzTWHFVVZKPxo4+PCP39Wa5Bay9txwrdt1ouV5jgFur52EFA5Al34eEmmPs9HO3\nul+x2y7SwEc0AehEB69HAwqQE+FATjKhEqlzpFxszOYk17Z40X5pAG45Sqx1iMzGIQz5RDS0X3Zk\nUBQnRyukIXKOZ944jl/cf2OQo73u7QZ87loOmJRSKyhGlQEY9Fk7yMp6u6+M9GEbEbkktMEA9Rzc\nLgGiGNxbBUg3C/NmTUTThS913wG02wtMUrsM8KB+beXzVFQzvzF7En78rWttv0MzRzESBzbc3w4Z\nKoIgiOgZrbaZUE6hU6fRzE4trpiGtW81qO8xG3VU2+LFM28cR4AD4zCABvePRrTUNSwZWos6fq2j\ncxJksWxlLW63FNQ2KmubEYnNM96v2LWKPfziiIjYpsedBz6iCUAnQ/B6NKAAOeEUcpIJlXAvkEaj\nuLSySCfcVd9+WeccLps3UzU+WufZL3Kdo1xzSCoZvmZqLhraLwcZFAA6w19dVqAbDRHg0rF1TtjL\npVjhMj8Pq3LqgLkvqyJySRDsSHOPaRk3ALhcAurPXVZfNxMfUSibNA5LK4uwwyDSoTXGfpGrRt0f\nkMZoAFA/B+33F8pBtrqBMnNgzd6rfS4S40qGiiAIIjpi3TZjRSin0Ox15Xmt3TCzU9oZyAzAg3Nn\nBq31YFO3NL7JAqdimoA0SWnVrWW4s7wwqN0r3s6oMgYKFqXcAHRCncPyuCyn64omAE3Ba4LQQ04y\noRLuBdJoFDmgMxpawalAQMSMCWODyqzdLgHL583Ehb4hvP+ZVEbMAbWEOcvF4HYJCARGMsJGww8A\nswpycOrCFXVtDEDVy6WSqqXJPMQOno/qofW252fWb+zWPCdAEuNSSqFbu/vR0tOvvnf25HFo7enH\n8XNSebedcBgA/MUtZZbfgfK5cs0+RA68eqgV2zWjMpx+f6FuoIwCK2afufE5Mq4EQRCjSyzbZuyw\ncwprW7w4d2kgpK1W7IqdjfPIbUY61uZhtcmaOipWofCBf8R9/7YfcFBKrfDwvGI89Z3rAeh7gu16\njLUoAeJQJdlm2ymjrFwC0/VdazHeJzgchqESTQA6EcHrVOupJzIHcpIzGLMLUzgXSKPRXFoplSlp\nRzxoBafOXRpAzaFW7Ko/H+Q8L60swu7POoOMgT/AsXz+TMyYMDYoq+qTI6zb69owJM81diOAU2O+\nBxwzX7PTaLMgMFQWT8BhTY8UANVBVvqWtOXg2sW7BWDW5Ktw+sIV1UEuKchBS3e/7hwFBkwY68Hs\nKVdhTmEugODvQHtTUXOoBecuDerWpL0Bcvr9hXMDZZUhMD7nNJIeqUEkQ0oQBBFMrNpmQh3DzLnV\nBb0FhuXzirFEvg+wsjFmNk4JNi+umCa99tbfALWvmK5lZdkHmJSbjaWVRSgEMGvSOBxz6CS7GLDU\n6ITDeeY9mgy9ttRcFDl21Z/HnMLcoO2XVhZh29Gz6txns/WmC5kgFkZIpOI9HDnJGUq0CojKD93M\naGr38+pj1dhR14atR89i82H9fGAGgDGmqj0LQnCJswigYnoeVswvVp/TGn6lhPmMzUzjO4b/Gc18\nOgLysT0uhq/NnICjzV6IkBzab143FXsau+APcDBZ1fKIwUE28oMFpdhVf9601Frk0Dn9HMDd5YXY\nuP9MUHa6p9+Hw81ePLzhADatXGD6PSg3FW8fa8c56J1kp6XxxhJ1pzdQVu819nzH8waDDClBEIRz\n4lE6a+aMa53hgMgxXa4YA2BZBm5s5Xl+72l8cKITIgf+cPYu015jMBfw8x7UtnixT7YFO+QqqjMX\nr+jeOmPCGHT2DkHkylxi+3MwnocSgDf77KLJ0Cu2dNgnQgTwyamLONLcY1rJtWnlgpRzKCIhE8TC\niNS9hyMnOcakSqQk0guT2Q999e2zLd9fVZKvjnQyilsxJhnVdW83YGllEcz0rBhG1KCVz/YHC0rR\ncL4XfzV+P+YfW45fZ5sfu3SwBgxS2bSomcf44FwpM320xQtwqZX5ppkTsGjOFOyqP48xHhc+OBGc\n1dbCAby4/ww456bvMxPyeqehA/d8ZRre/tN5iJyDMaabnejTKIKb3Uhsr2tDY2efbp+zJ4/Dbx64\nKaLSaqc3UFbv1T7n5PekjP2I5Hc3GoY0Vf52CYIgnBCr0lm7a6NVENVK28I4mnHtm/WYy4+jKfvX\n5gf/hw7AM1Zdx3O7T6qBacUWTBk/BsBIJvmG6Xn4Hw9frfYb//zNevjkKHltixc1h1p1gXflPNwC\ng08OlCszk4039NFk6JXP5LndJ/HJqYu29szY8pSutilTxMIynVQNhpCTHENqW7xYvuGAWiKz2SIr\nmAxEemGK5IdeXVYAt0sIEq1S/EOl19asB5gD2Hy4FeXT89Sy5iYb8Y7aHzbjkY2SKmSWe2T80rq3\nG3Rl4UBwJlQrMqaocQdsPOWAmSdsQ3N3P5q7+/HEbWXoG/Jjy9GzutcFBrzX0IG+AR9eOdCsu5FY\n93aDacZ64rissHvHwy3NBsxvtozP2f2ezMZ+hPO7i7chTdUoJ0EQzkhnRyOeONGvsAq4Gm2E0Rat\n2HUjVngsDrxWXz5ttCEMIzakuqwAH3zWCRGSVsgTC69Wj13b4sUN08ar5dgBDqzZWW9a5gxZUItz\nYFi278O+4FJx7fQNpf0oHFv6429diyPNPY7sWbrbpnhUPBDJRyzu4RJxDScnOYa8sPe0Gq30BThe\n2HsaGx6dm+BVmRPphUkbbbUa06DtT1UMidnYI4FBzfRe7BvC14onoK71kpSd5SMtvgEOyZi6AFgo\nVC8a9wbuLi/EUxbnNacwN2QmdFDua/b5RayYX4zpE8bit/ub0HPFF3Q8lwAIjKnftx1jPQIGfCPn\n33C+F9VlBRA1TnbeWDcuD/hxrO2yrrfK5xfVHm6zI527PBhSaERb4qUtb7cj3ItRqN9TJOOpjGuI\n1pDanVOqRjkJgghNujsa8WSHrPnBITmMz+0+GXTtdhpwrS4rQIF7AEdcPzJ9/d6hX+IEK8OWVV+X\nRDc1bNesQ+EHC0pRVZKPmkOtUCysCKCxo0/dZlttm6qcrSByHnSNVxS2lX5haPZntJnKdspvSmAM\n6+6rCMpOWxGOPcsE2xSrigcieYn2Hi5R13BykmNIZ++g7eNkI+ILkxxtlf4/glHAA4zBHxDlsuLg\n3ay8tQynL17BByc68Z6sbM0YMD1vDPoG/egd9KPZptf4usBm3F1eKClhD/bj+X1NAICnvnN9yKyn\n0WF6v6FDfY0DyM12Y/Xts7G3sQuHr+h7kxkAF2NYOGeKqdiYkaIJY/GFRnm7YFwWqssKdPOVLw/4\ng46hZFsXV0zDkeYe3UxphXPeAfzzu422sxQVUZQ1O+shcqm83TSKrvlsIrkY2f2eIhlPZaWMGgmh\nzolKvggifUkHRyMRWZTaFi+2Hj07MlEB1n20IVmbhyoAR0wC3U/ftB8X+4ZwY2421mpmFCvnnJ+T\nhW21bTpbywFs3H8Gd5YXYlf9ed3+thxpRWNnn86pljRQpH9nmVzj1dFMnEOQK8mUqidv/3DQmrW/\nKZFzPGOVnbYgnMAC2SYiHYjmHi5R13BykmOIUWFx1qRxCVxNfNBGWwMB/Q/1YFP3SMQ5wMEg9+ty\nvWdXOD4b9391BnqH/EFOJufAJ4NLpAdjgo//n0X/Be9O/yscaOrGrePHqKVOCu80dKijHbQYM9wP\na8riN61cgN2fd+nef0De7/1fKwpSuFbO78yFL+FxjfQVWyWVL1zRG9juK8OoKslH+fQ8S0VOQQCW\n3VyszpWeU5iL53afxMdfXAx6ryJeZjdL0ds/DJHzkKIkgOF79MXmYhRuFDHWF8RQ+6OSL4JIX1Ld\n0UhUFkXRE9ES9jV5bZ758wufAm7/KQDArBtZe84CG9EV0eIXpYzw4oppOts4dfwYdfSiAgdw303T\n0X1leERBW3OsdW83QJQd5MdumYVXDjTb/l6qywrAZKcakNqvwpln7BSyTQSRuGs4OckxpNvEGUo3\n7H6ofQM+nVFSEs5G01ZdVhCk8pyHL3FszErL45YN1WD5vGJUTM/Dxjfr4Q9wAJch6JPZuLu8EECw\nU6y9wbj1mskYlj3a4QDH83tP43TXl7r9ZLsFAMCeRr3zrOXUhStwuxiWzStG/5Bfne1sZNG1k3Wv\nLa6YBiA4qFIyMWdkzjKHOlcaGOljMnOSFbr6hixLr7Xfm8slWIqSAFJEXZs5cFKe7YRwoohWv7NI\nsylOLrBU8kUQ6UmqOxqJyqIo7VXDmgiwIz2JHauAP202f22teWDYiPacleyumVBmfk4WVswvRmv3\nFbzT0IG7ywtxZ3kh9py8EKSD8vafziMgchw4LQXBlfJo7bE45+gb8of8vVSV5OOb103B+3IVHBD+\nPGOnkG0iTYFMJ1HXcHKSY0jBuCzbx6lAqAuRUrqrm2co03C+V/debuIgA8Cbx9rVsmG7cuobBl9C\nv5xOFpgk4MWgz9iKHJhXmo+uviGpJ/k71wdF3ZdUFukyo2cu6B3irt7BoHXOnirNLG4yvNdIIMAx\nY8JYvKcp11YoLcjBytuuxor5xei5MozDzT2YVzoRK+YXo7bFizeP6Z3qqeOz0dk3aNk7XFWSjydu\nK1PLyo181NiFD050hhRXOXdpQB3HZXbD5e0fVkvBGWA5yzGemF0Qo8mmpPpNMkEQ0ZHKjkY4WZRY\nzqGvKsnHg3NnouZQqzM9Caus8Zg84KlWx2sB9PonbhfD2j+vQEP7ZWw+3KreAzBI9qrmUCs27j8D\nkXO8cqAZd5YX4oGqItQc0h9TCcz7Ra4T79KKi3IAW4+exZLKItvJHQBw+xy9k1wx3eL8iaggTQEC\nSMw1nJzkGGKc1Wd8nOw4uRApZUnDfhFHmnt0zlP5tPG6TKeZg8wA/L1rE55wv2W5jtLBmqDnRI1R\nNFJZnI/csR7k52Rh/UencOzsJd2IiIt9Q7rM6JnufnVbt4th2c3FONHRoEadXQyqAnbZ5Ktw6oL1\n9+hxSeJlx85egnYEBSBltVfML8azfzyBffLnsu+Li3j2jyeQO9YT1F/c0+8L2TtcXGBews8gOeza\nsRhWPdiAJMbi84twCQztlwZQ2+JV36+9OeEA9n8RYQ9alBgviNFmU1L5JpkgiMzFaZAvXGdC2/Or\n2HXjdksqi7BdthemehIn3wVqHjI/wM+6AHe2dJwQApOmaPRP5hTmYsX8YuRmu7Hh4yZwDmR7pMkU\na3bWqw7wsGwbllYW4bUjZ9XnlZ5kxe5qxbuqSvKx8NrJqsOrHcdo97mduzQwslSY9y7HgnTOopqJ\nvcZyNjVBRAM5yTFkqmFWn/Q4dXByIbJ7T12r12y3KnZZ49LBGowf40bvoD/oNUUBm3OujonSOpgb\n959RRTaMuFwCLhkMl6gxmsvmzlRLrp7ZWY+AyOFyCep7Vy28Gh82dsnl3SPMnnIV5s2aiIrpeTjY\n1I0yk/5zpa/5jU/P6Z5/49NzWP9IlU64C5DKr+vbL6vnYvYd/OM7J4LPkUknI4r6sRgKtS1eLHvh\nP+AXAbcAbFn1dbz6WLWq/LnpcCu217VhzT3l8PYPSxlspnaUW67FSLwNear3FRIEQUSKkyBfOHoS\nZj2/Znbd6KA3dvThud0n8Yezd1kvRFNSXdvixcMvHlSv21YCk2bnouif+GX9EwB46ZMz4FyqLlNs\nlnYco8CYaoMeu2WWWnnlcjHcc+M0vHmsHZzrxbtqW7z4UKNLwiG1j4X83DT9Xhyxa02yOl66ZVGt\nxF5jOZuaIKKBnOQYsmjOFFWlWXmcSji5ECllST6/CBhKgk91BZcm2znGgD5rnJ+TZeoke9wC1t4r\nGUPFSK/ZWQ9R1KtQGmEAvlqUFyS85XYxiCKHRy7FBqQIMFcEOAyCZN+cMwVNF75Ec08/uLzdX3xj\nFhraL2Ptm/VqxtWI0tc8cVwWOnqH1OeLJ+agqiQfv7z/Rvzs9eMQITmvV08ahxf3n1H3ZRyx9ewf\nT+CSQQV78lVZ+Fpxvu53p4zFUHh+72korVl+UXr84qNz1ZsQkUvRdyWDbRRJMXO8jYyGIaeSaYIg\nCGvM9CSsgpdmPb8M3PRarzjo2z8+hhUf3AZTq/7EJ0BhRdDTO+ra1CqtUAKTxnMZyfpKj7fXtan9\n0QEO1LdfxtLKImR7pBGHgiCNYlLaczZ+ckbdnz/A8fafJAfZJTCsuadcJzoaMJR27f68y1QEVNe/\nbOjVjkcmOZ2zqLpzC3AA5gkCsv1EoiAnOYa8/p9tQY+dzs1LBpxeiERZPCMgcqx9c6SvZ/xYD3r6\npehrqKyxGX2D5pHbqyeNC/ocH7p5JhikUU1WPbrZHgFD/uDZU4vmTMGU3GydY2sWIFAi4IqBdwvA\n8nlSuZe2vMuK2VNzUdvi1QUPBAY8uVgyvCvmF6vzm5WSMa2hfnDuTN138I5J3/OsSeMw4AvonjP2\nhncZRpEpj7XnLI3pGlEjV26YXC4BD1QVqSrbVmiN3XAcDTmVTBMEEQmpXLLqdO1aPQmBSU7k2rca\nTLO4yvVfcS4fu2UWcsd6dNliRWRyxa4bAQBLTY559VANfnLXHKwuNO/fNVpJp+JW3v5hyMXWEDSP\ntVzsG8LBpm41o6z9fKQgsP5oyu0A5xz17ZdVocvqsgL1WAqnu75U25C0n79OBFPOfgYC8ctwatuf\njIHzZCScv7NwPkuy/UQiICc5higD7K0epwKhLkQHm7oR0Bgebe/Oniv3m45tAoDvDT+Fj8Wv2B57\n6vgxqpOt5URHn85YaTOWX5kRLJThlscnLaksQmNHH461Hde9zgApIi1HtZWspzFAsP6jU1LGXMYv\njsxmDOUgK33NxhEaZj3VANDQflmXvXULTM1yK9xdXhgUEJg9NRcV0/N0veDl08br3rPs5mLdZ7Ds\nZingoD1npSdNuZkyu+mwwyzqTxAEkQzUtniDxv6lyg2307XXHGrFew0dENhI9c/FviHLLK4iwqlU\nEL1yoBmvPlaNxo4+PP36cQDcsqT6Wd9yPB/4czBIwWg7x21pZRG2HT2rrn9pZZGpI2V8rrqsAB7X\niHCXcoyttVJ/tNvFsKexC7stxCrNbJBbgJpJ3lbbppb2rrmnPOi9HFBLvI1VUsbyczMh05ii6c1O\nZsKtKDPedwHmPckEkSjISY4huYae2twxqffxGg2VqeFyC6rhne3qwOq9K4C95vt7+qb92HS41Tgq\n2ZSZE3PweUefaaRZccSNpUetPf269xWOz8b6R6p0NwKt3VdU59LtYpicm63r23ph72kM+AJYXDFN\np2ZZXVaglnMrnOrsM53XWDRhDDp6h6SeZk3JFwBd+TLnI+di7MdxuwT4A1J/mHZ7hae+cz0+O9+r\nioABkpqmMv5CETNR1D2V7ZUsvGLItVl5Y1DE7D1OMIv6pwupnIEiCAK6Mt3hAMf2OMyzjRdO1l5z\nqFV2bCXuvGEqnlh4NbbX6avbOPTXM2//cFAv8o0f/19oHnPYdC3fm/keFldMQ0P9edw/Lst05rCR\nqpJ8bFq5wHIk46uPVVs+Z3QOq0ryselx51MatNnhu26YilULr8bBpm60XxpQFbuHfSJ21Z8Puu9w\nMekewKzcefXts1UbvkYeSXmgqdvRFIhw7Ym2N9vYCpZsRFIabrwHSdZzIzKT1PPikpjVt1+jM1Sr\nb78mgasJH2MUcM095aripVtTdrvp8WpUvVxquZ/SwVcBMLgYIB5yPvZh94lOUwfZLUA1rmalR9oZ\nxPd/dUbQRfbO8kK89B/N8MkCJbnZbl3fltLPq2RjlRFNB5u6MasgR6duPSR/NoM+fRn3X95+jVo6\nbRyhse6+CrWHOksTddcalIDIsWzeTMyYMNbWeM4vK8D+UxfVcjrFGc0d6wFgLbK1Yn6xreNrp1ru\nhOqyAmR70k9YI51FUwgiUzDm35I7H6fHydp31Z/XPR70BVBVkh9UzTY+2x1k45Vy19PZ5sHu83wi\nFgz9GwBgni+AOYW5mFOYq+7Hib3QOkLrPzoV5EiduzSgm0ihZHDtnMOK6Xm2GipGm7Rq4dXqOhQH\nGZDuAcqnjcehMz1q8F8A8PitZWqVldVxXth7Wi3p9gc4Xth7GhsenWv5OURiT1JJtCqV1koQTiAn\nOYa0dl+xfZzsGKOAu+rP6/pMb6r9GaqO7bHcfv3CWvzTu43q44CZx2uDVQXzspuLdYaEa/7LyXbr\nosUN53t144yU89Ia24bzvUHK0gpbjrTqbgCYobxpQVkB1txbjoNN3egb8KHhfK8u82pm8LS9x1oH\nWCuC5nIJIft+lW3MjFC0xikW45XSUVgjnUVTCCJTWFJZhNc0Jb/GVpZkZkllkVpirBWb1LK4Ypqu\n5UbpJdZW9DBI9lF7PWOf7cDnrp8DruDjzh78PfyGW8TDzdKkhGU3FwddFwFnpbLGQHd+Thae233S\nVLDSTCdE62TeXV6IT89ewt2ayikFO5tk7N3OHevBpsersaOuDRySA64diWXVftR0QS9WanxsJNJM\na6rY1lRaK0E4gZzkGPJabVvQYzN1xGTF6GgtrpiGI809+Ny13HKbmsXHVQex74/B44nMMApkWD2v\n9Dtpbwp21LXJKohSP/TFviFV2VIE8Mmp4Jm+Zud16EyPlFk2lFN/dr5XVeMUOcAMpdV9Q/6IBCSM\n29S2eLGjrg2iqCqJON6PmRGK1jjFIgKcjsIaFBkniPSAG/6fKmhLjK2u7VYtNdq+XA4pY6qz6S3B\nxzMKa7qFEcErQPr3yc4+3XWxb8CHZS8cQEDkyPY4yJBqyqj3NHapNh0AigvGqedtphOiZJwHfaJa\nRfb8viYUFwQLfFrZJLPrul2229s/rLZiaculyyZfpas0K5t8lfU5WxzXCalkW1NprQQRCnKSY4jA\n7B8nO1qjtHpvFbALWGESYd4WuA3/zfcEAMC9c0Td2qiqbIXVTcqq28rQO+RXVauVLK0xi6xlUm42\nfrCgFJuPnsWlfp+jOY8AIIqiquRcNGEM2i5Jis/+AMeFviHVkIHpnehY3GDVHGoNUsf2awTQQtHY\n0aeWgRl7eay2D9UHZedkp3NPbjSfC0EQqcGOujZdWazTMUTJghPHw6ylpqF9ZF5xAS7jqcPVeMrE\npt89/I+ouvnrcg+zvpVo/NgsfDnoU/uiAamyTCv6+Mwbx9XKsVDzmbWVXT6/GJR9PdWV4DV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B0miISSVJMnYk1tixcPvzgyItDq2jpqnP4Q+MN3zV9b4wUEQX0YzbUxnKCq0RHVinxp+48jOY7R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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc658198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "# 创建一个模型的实例\n",
    "model1 = myLinearRegression()\n",
    "\n",
    "# 使用训练集对模型进行训练，传入训练集的LotArea和标记SalePrice\n",
    "model1.fit(train_data['GarageArea'], train_data['SalePrice'])\n",
    "\n",
    "# 对测试集进行预测，并将结果存储在变量prediction中\n",
    "prediction1 = model1.predict(test_data['GarageArea'])\n",
    "\n",
    "def MAE(y_hat, y):\n",
    "    m=y.shape[0]\n",
    "    mae=sum(abs(y_hat-y))/m\n",
    "    return mae\n",
    "\n",
    "def RMSE(y_hat, y):\n",
    "    m=y.shape[0]\n",
    "    rms=(sum((y_hat-y)*(y_hat-y)/m))**0.5\n",
    "    return rms\n",
    "\n",
    "mae1 = MAE(prediction1, test_data['SalePrice'])\n",
    "rmse1 = RMSE(prediction1, test_data['SalePrice'])\n",
    "print(\"模型1，特征：GarageArea\")\n",
    "print(\"MAE:\", mae1)\n",
    "print(\"RMSE:\", rmse1)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# 创建新的图\n",
    "plt.figure(figsize = (16, 6))\n",
    "\n",
    "# 创建子图1\n",
    "plt.subplot(121) # 121分别代表，生成1行2列个图，这是第1个\n",
    "# 其中的参数为横轴值，纵轴值，label为此条曲线的标签， '.' 表示画出的图的图形为散点图\n",
    "plt.plot(train_data['GarageArea'].values, train_data['SalePrice'].values, '.', label = 'training data')\n",
    "# '-' 表示画出的图形为折线图\n",
    "plt.plot(train_data['GarageArea'].values, model1.predict(train_data['GarageArea']), '-', label = 'prediction')\n",
    "plt.xlabel(\"GarageArea\")\n",
    "plt.ylabel('SalePrice')\n",
    "plt.title(\"training set\")\n",
    "plt.legend()\n",
    "\n",
    "# 创建子图2\n",
    "plt.subplot(122) # 121分别代表，生成1行2列个图，这是第2个\n",
    "plt.plot(test_data['GarageArea'].values, test_data['SalePrice'].values, '.', label = 'testing data')\n",
    "plt.plot(test_data['GarageArea'].values, prediction1, '-', label = 'prediction')\n",
    "plt.xlabel(\"GarageArea\")\n",
    "plt.ylabel('SalePrice')\n",
    "plt.title(\"testing set\")\n",
    "plt.legend()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 选做：剔除训练集中的离群值(outlier)，然后重新训练模型，观察模型预测性能的变化\n",
    "###### 提示：可以使用下面的代码处理数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xcbf0518>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4tweNwnUP6gnm7raFge/ls4ApdTyMEEIqGVEq12DSyqKzs1P19PSU+jIqiqCE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\naz6lK/zst8fzvm4ny6/5E8QvXEZyQocOoxVvRBP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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc322c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "t = train_data[(train_data['LotArea'] < 60000) & (train_data['LotArea'] > 0)] # 将训练集中LotArea小于60000的值存入t\n",
    "t = t[t['SalePrice'] < 500000] # 将t中SalePrice小于500000的值保留\n",
    "\n",
    "# 绘制处理后的数据\n",
    "plt.figure(figsize = (8, 7))\n",
    "plt.plot(t['LotArea'], t['SalePrice'], '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
